Deep Learning Projects for Beginners
Are you looking for Cool Awesome Deep Learning Projects to Finally Begin? Then you are at the right place to get started.
What is Deep Learning?
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervisedor unsupervised
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts.
The “deep” in “deep learning” refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path(CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. Source: Wikipedia
Below are the Complete list of Awesome deep learning projects ideas for final year students sorted by categories.
Before starting this I would suggest to see Machine Learning Projects or Ideas for Beginners or Final Year Students.
Simple Deep Learning Projects For Beginners
There are a number of ways to learn in the field of Deep learning and mostly with theory.
On the off chance that you are a beginner/software engineer then you as of now have the skills to deteriorate problems into very small projects and to model little tasks so as to learn new technologies, libraries and techniques. There are few Essential skills for any expert developer and these skills can be very useful to begin in machine learning, today.
Below are some of the fun Deep learning projects which can be utilized by Beginners or Final year students. You’ll appreciate learning, remain spurred, and gain quicker ground.
These Projects enable you to enhance your connected Deep Learning skills rapidly while allowing you to investigate an intriguing point.
If you are a machine learning beginner and looking to finally get started in Machine Learning Projects I would suggest to quickly go through below and Enjoy!
Before you start working on any of the Projects I would suggest you need to see few things listed below:
1.) Laptop or PC
A decent Laptop configuration for this I have listed 10 Best Laptop for Machine Learning Programming in 2019
2.) Books
Top 15 Best Deep Learning and Neural Networks Books
Top 7 Free Must-Read Books on Deep Learning
3.) Tutorials
A Complete Guide on Getting Started with Deep Learning in Python
HOW TO START LEARNING DEEP LEARNING IN 90 DAYS
Top 50 Awesome Deep Learning Projects GitHub
10 Free New Resources for Enhancing Your Understanding of Deep Learning
Learn TensorFlow and deep learning, without a Ph.D.
The Ultimate List of Best AI/Deep Learning Resources
Top 10 Best Deep Learning Videos, Tutorials & Courses on YouTube
4.) Courses Certifications
Deeplearning.ai Announcing New 5 Deep Learning Courses on Coursera
15 Deep Learning Open Courses and Tutorials
10 Best Deep Learning Global Certifications and Training
10 Best Advanced Deep Learning Courses
Top 7 Best Deep Learning Online Courses
5.) Podcasts
Top 15 Best Podcasts on Machine Learning & AI that you Must Follow
Fun Hands-On Deep Learning Projects for Beginners/Final Year Students (GitHub)
What is GitHub?
GitHub is a code hosting platform for version control and collaboration. It gives you and others a chance to cooperate on projects from anyplace.
GitHub shows basics like repositories, branches, commits, and Pull Requests.
To finish this instructional exercise, you require a GitHub.com account and Web access.
Jump into deep learning Mini-Projects for students curated by individuals on GitHub, or add your own resources to these lists.
There are 3 Broad Categories created below:
1.) Computer Vision
2.) Audio, Speech Processing
3.) Text Processing
Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.
Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions.
Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, and image restoration. Source: wiki
Image Classification
a.) Real-time face detection and Emotion / Gender classification
GitHub Repository : Access Code Here
Image Generation
a.) Restore colors in B&W photos and videos
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
b.) Handwriting Generation From Text
GitHub Repository : Access Code Here
c.) Image Completion with Deep Learning
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
d.) 3D Face Reconstruction from 2D Image
GitHub Repository : Access Code Here
e.) Text-to-Image-Synthesis using Generative Adversarial Network
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
f.) Generating Human Faces – Progressive Growing of GANs for Improved Quality, Stability, and Variation
GitHub Repository (TensorFlow) : Access Code Here
Image Recognition
a.) Face Alignment – Detect facial landmarks using a face alignment network
GitHub Repository : Access Code Here
b.) Visual Question Answering – QA from Image
GitHub Repository : Access Code Here
c.) Evaluating Handwritten Math from Image
GitHub Repository : Access Code Here
d.) Real-time multi-person pose estimation
GitHub Repository (Tensorflow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
f.) Real-time analysis of behavior of crowded area
GitHub Repository : Access Code Here
2.) Audio, Speech Processing
Audio signal processing or audio processing is the intentional alteration of audio signals often through an audio effect or effects unit. As audio signals may be electronically represented in either digital or analog format, signal processing may occur in either domain. Analog processors operate directly on the electrical signal, while digital processors operate mathematically on the digital representation of that signal.
Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields. Source: Wiki
Audio Classification
a.) Urban Sound Classification – Classify Type of Sound
GitHub Repository : Access Code Here
Audio Generation
a.) Restoring Sound in a video – Lip Reading
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
b.) Learning Lip Sync from Audio
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
c.) WaveNet – DeepMind
GitHub Repository : Access Code Here
d.) Magenta – Make Music and Art Using Machine Learning
GitHub Repository : Access Code Here
e.) Composing Music
GitHub Repository (Keras) : Access Code Here
3.) Text Processing
In computing, the term text processing refers to the discipline of mechanizing the creation or manipulation of electronic text. Text usually refers to all the alphanumeric characters specified on the keyboard of the person performing the mechanization, but in general text here means the abstraction layer that is one layer above the standard character encoding of the target text. The term processing refers to automated (or mechanized) processing, as opposed to the same manipulation done manually.
Text processing involves computer commands which invoke content, content changes, and cursor movement, for example to
- search and replace
- format
- generate a processed report of the content of, or
- filter a file or report of a text file.
Text Generation
a.) Text / Word Generation With LSTM Recurrent Neural Networks
GitHub Repository : Access Code Here
Natural Language Processing
a.) End-To-End Memory Networks for Question Answering
GitHub Repository : Access Code Here
Text Classification
a.) Sarcasm detector
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
b.) Sentiment Analysis
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
Further More Deep Learning Projects To Explore
a.) Predicting Cryptocurrency Prices
GitHub Repository (Keras) : Access Code Here
b.) A full demo of the Pokedex + real-time deep learning model in action can be found below:
c.) Predicting Earthquakes
GitHub Repository (TensorFlow) : Access Code Here
d.) Deep Learning to play Flappy Bird
GitHub Repository (TensorFlow) : Access Code Here
GitHub Repository (Keras) : Access Code Here
Final Words
I can just say I’m amazingly urge on DL Projects, some of them you can run them on your PC, some of them you can play in tensorflow play ground or effortlessly on Deep Cognition’s platform in the event that you would prefer not to install anything, and it can run on the web.
For making this awesome Open Source commitments and for all the others that will come in future. Attempt them, run them, and get propelled. This is just a little case of the astonishing things DL can do, and is dependent upon you to take this and transform it into something that can enable the world to improve as a place.
Never surrender, don’t be scared of projects, Get hands-on, Learn—Practice—Learn we require everybody to be keen on heaps of various things. I believe we can improve the world, enhance our lives, the way we work, think and tackle issues, and on the off chance that we channel every one of the assets we have right presently to influence these area of information to cooperate for a more prominent great, we too can have an immense constructive outcome on the planet and our lives.
Good Luck!
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